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| 1 | Multi-view Clustering: A Survey显示文摘In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view Clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering. | Yan Yang Hao Wang | 2018 | Big Data Mining and Analytics2018，1，2： | 20 |
| 2 | DEEPEYE: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization显示文摘A Non-negative Matrix Factorization(NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction results. We present novel iterative rules to construct matrix factors that carry important network features and prove the convergence and correctness of these algorithms. Finally, we demonstrate how latent NMF features can express network dynamics efficiently rather than by static representation,thereby yielding better performance. The amalgamation of time and structural information makes the method achieve prediction results that are more accurate. Empirical results on real-world networks show that the proposed algorithm can achieve higher accuracy prediction results in dynamic networks in comparison to other algorithms. | Nahla Mohamed Ahmed Ling Chen Yulong Wang Bin Li Yun Li Wei Liu | 2018 | Big Data Mining and Analytics2018，1，1： | 11 |
| 3 | Relation Classification via Recurrent Neural Network with Attention and Tensor Layers显示文摘Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8dataset show that our model outperforms most state-of-the-art methods. | Runyan Zhang Fanrong Meng Yong Zhou Bing Liu | 2018 | Big Data Mining and Analytics2018，1，3： | 9 |
| 4 | A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering显示文摘Due to the widespread availability of implicit feedback(e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback,implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems,some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and contentbased recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders(DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods. | Yu Liu Shuai Wang M.Shahrukh Khan Jieyu He | 2018 | Big Data Mining and Analytics2018，1，3： | 7 |
| 5 | Big Data Analytics for Healthcare Industry:Impact,Applications,and Tools显示文摘In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as big data. The health industry sector has been confronted by the need to manage the big data being produced by various sources,which are well known for producing high volumes of heterogeneous data. Various big-data analytics tools and techniques have been developed for handling these massive amounts of data, in the healthcare sector. In this paper, we discuss the impact of big data in healthcare, and various tools available in the Hadoop ecosystem for handling it. We also explore the conceptual architecture of big data analytics for healthcare which involves the data gathering history of different branches, the genome database, electronic health records, text/imagery, and clinical decisions support system. | Sunil Kumar Maninder Singh | 2019 | Big Data Mining and Analytics2019，2，1： | 6 |
| 6 | Location Prediction on Trajectory Data: A Review显示文摘Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few. This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications. First, we introduce the types of trajectory data and related basic concepts. Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction. We also discuss and analyze the advantages and disadvantages of these algorithms and briefly summarize current applications of location prediction in diverse fields. Finally, we identify the potential challenges and future research directions in location prediction. | Ruizhi Wu Guangchun Luo Junming Shao Ling Tian Chengzong Peng | 2018 | Big Data Mining and Analytics2018，1，2： | 5 |
| 7 | A Novel Clustering Technique for Efficient Clustering of Big Data in Hadoop Ecosystem显示文摘Big data analytics and data mining are techniques used to analyze data and to extract hidden information.Traditional approaches to analysis and extraction do not work well for big data because this data is complex and of very high volume. A major data mining technique known as data clustering groups the data into clusters and makes it easy to extract information from these clusters. However, existing clustering algorithms, such as k-means and hierarchical, are not efficient as the quality of the clusters they produce is compromised. Therefore, there is a need to design an efficient and highly scalable clustering algorithm. In this paper, we put forward a new clustering algorithm called hybrid clustering in order to overcome the disadvantages of existing clustering algorithms. We compare the new hybrid algorithm with existing algorithms on the bases of precision, recall, F-measure, execution time, and accuracy of results. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values. | Sunil Kumar Maninder Singh | 2019 | Big Data Mining and Analytics2019，2，4： | 5 |
| 8 | A Tutorial on Support Vector Machines for Pattern Recognition显示文摘 | Christopher J.C. Burges | 1998 | Data Mining and Knowledge Discovery1998，，2： | 5 |
| 9 | An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor显示文摘The Collaborative Filtering(CF) recommendation algorithm, one of the most popular algorithms in Recommendation Systems(RS), mainly includes memory-based and model-based methods. When performing rating prediction using a memory-based method, the approach used to measure the similarity between users or items can significantly influence the recommendation performance. Traditional CFs suffer from data sparsity when making recommendations based on a rating matrix, and cannot effectively capture changes in user interest. In this paper, we propose an improved hybrid collaborative filtering algorithm based on tags and a time factor(TTHybridCF), which fully utilizes tag information that characterizes users and items. This algorithm utilizes both tag and rating information to calculate the similarity between users or items. In addition, we introduce a time weighting factor to measure user interest, which changes over time. Our experimental results show that our method alleviates the sparsity problem and demonstrates promising prediction accuracy. | Chunxia Zhang Ming Yang Jing Lv Wanqi Yang | 2018 | Big Data Mining and Analytics2018，1，2： | 4 |
| 10 | Clinical Big Data and Deep Learning:Applications,Challenges,and Future Outlooks显示文摘The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly,based on the analysis of the characteristics of clinical data, various types of clinical data(e.g., medical images,clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks.Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine. | Ying Yu Min Li Liangliang Liu Yaohang Li Jianxin Wang | 2019 | Big Data Mining and Analytics2019，2，4： | 4 |
| 11 | Spreading Social Influence with both Positive and Negative Opinions in Online Networks显示文摘Social networks are important media for spreading information, ideas, and influence among individuals.Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the 'word-of-mouth' effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set(MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of ?. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different realworld data sets that represent small-, medium-, and large-scale networks. | Jing (Selena) He Meng Han Shouling Ji Tianyu Du Zhao Li | 2019 | Big Data Mining and Analytics2019，2，2： | 4 |
| 12 | Intelligent and Adaptive Web Data Extraction System Using Convolutional and Long Short-Term Memory Deep Learning Networks显示文摘Data are crucial to the growth of e-commerce in today's world of highly demanding hyper-personalized consumer experiences,which are collected using advanced web scraping technologies.However,core data extraction engines fail because they cannot adapt to the dynamic changes in website content.This study investigates an intelligent and adaptive web data extraction system with convolutional and Long Short-Term Memory(LSTM)networks to enable automated web page detection using the You only look once(Yolo)algorithm and Tesseract LSTM to extract product details,which are detected as images from web pages.This state-of-the-art system does not need a core data extraction engine,and thus can adapt to dynamic changes in website layout.Experiments conducted on real-world retail cases demonstrate an image detection(precision)and character extraction accuracy(precision)of 97%and 99%,respectively.In addition,a mean average precision of 74%,with an input dataset of 45 objects or images,is obtained. | Sudhir Kumar Patnaik C.Narendra Babu Mukul Bhave | 2021 | Big Data Mining and Analytics2021，4，4： | 4 |
| 13 | Event Detection and Identification of Influential Spreaders in Social Media Data Streams显示文摘Microblogging, a popular social media service platform, has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for detecting newly emerging events and for identifying influential spreaders who have the potential to actively disseminate knowledge about events through microblogs. However, traditional event detection models require human intervention to detect the number of topics to be explored, which significantly reduces the efficiency and accuracy of event detection. In addition, most existing methods focus only on event detection and are unable to identify either influential spreaders or key event-related posts, thus making it challenging to track momentous events in a timely manner. To address these problems, we propose a Hypertext-Induced Topic Search(HITS) based Topic-Decision method(TD-HITS), and a Latent Dirichlet Allocation(LDA) based Three-Step model(TS-LDA). TDHITS can automatically detect the number of topics as well as identify associated key posts in a large number of posts. TS-LDA can identify influential spreaders of hot event topics based on both post and user information.The experimental results, using a Twitter dataset, demonstrate the effectiveness of our proposed methods for both detecting events and identifying influential spreaders. | Leilei Shi Yan Wu Lu Liu Xiang Sun Liang Jiang | 2018 | Big Data Mining and Analytics2018，1，1： | 4 |
| 14 | Heterogeneous Network-Based Chronic Disease Progression Mining显示文摘Healthcare insurance fraud has caused billions of dollars in losses in public healthcare funds around the world. In particular, healthcare insurance fraud in chronic diseases is especially rampant. Understanding disease progression can help investigators detect healthcare insurance frauds early on. Existing disease progression methods often ignore complex relations, such as the time-gap and pattern of disease occurrence. They also do not take into account the different medication stages of the same chronic disease, which is of great help when conducting healthcare insurance fraud detection and reducing healthcare costs. In this paper, we propose a heterogeneous network-based chronic disease progression mining method to improve the current understanding on the progression of chronic diseases, including orphan diseases. The method also considers the different medication stages of the same chronic disease. Extensive experiments show that our method can outperform the existing methods by 20% in terms of F-measure. | Chenfei Sun Qingzhong Li Lizhen Cui Hui Li Yuliang Shi | 2019 | Big Data Mining and Analytics2019，2，1： | 3 |
| 15 | A Brief Review of Network Embedding显示文摘Learning the representations of nodes in a network can benefit various analysis tasks such as node classification, link prediction, clustering, and anomaly detection. Such a representation learning problem is referred to as network embedding, and it has attracted significant attention in recent years. In this article, we briefly review the existing network embedding methods by two taxonomies. The technical taxonomy focuses on the specific techniques used and divides the existing network embedding methods into two stages, i.e., context construction and objective design. The non-technical taxonomy focuses on the problem setting aspect and categorizes existing work based on whether to preserve special network properties, to consider special network types, or to incorporate additional inputs. Finally, we summarize the main findings based on the two taxonomies, analyze their usefulness,and discuss future directions in this area. | Yaojing Wang Yuan Yao Hanghang Tong Feng Xu Jian Lu | 2019 | Big Data Mining and Analytics2019，2，1： | 3 |
| 16 | The research progress of acupuncture and moxibustion in the treatment of peripheral facial paralysis显示文摘As a common and frequently-occurring disease in clinic,peripheral facial paralysis is worth paying attention to.In fact,as one of the methods for the treatment of peripheral facial paralysis,acupuncture has been widely promoted and applied in clinic,and has been recognized by doctors at home and abroad.However,there are many other factors that affect the curative effect in clinic due to their different operating methods.Therefore,there are still many disputes in the treatment of peripheral facial paralysis.The relevant literatures in recent years were searched and consulted in order to understand the current situation and provide routine treatment methods for clinical acupuncturists to treat this disease.And we intended to analyze and introduce from acupuncture manipulation,warm acupuncture,electroacupuncture,giant acupuncture,fire acupuncture,acupuncture combined with massage,acupuncture timing and other treatments,and different classification of acupuncture points and methods. | Ming-Zhu Jiang Xin-Yue Guo Run Li | 2021 | Medical Data Mining2021，4，1： | 3 |
| 17 | Attention-Aware Heterogeneous Graph Neural Network显示文摘As a powerful tool for elucidating the embedding representation of graph-structured data,Graph Neural Networks(GNNs),which are a series of powerful tools built on homogeneous networks,have been widely used in various data mining tasks.It is a huge challenge to apply a GNN to an embedding Heterogeneous Information Network(HIN).The main reason for this challenge is that HINs contain many different types of nodes and different types of relationships between nodes.HIN contains rich semantic and structural information,which requires a specially designed graph neural network.However,the existing HIN-based graph neural network models rarely consider the interactive information hidden between the meta-paths of HIN in the poor embedding of nodes in the HIN.In this paper,we propose an Attention-aware Heterogeneous graph Neural Network(AHNN)model to effectively extract useful information from HIN and use it to learn the embedding representation of nodes.Specifically,we first use node-level attention to aggregate and update the embedding representation of nodes,and then concatenate the embedding representation of the nodes on different meta-paths.Finally,the semantic-level neural network is proposed to extract the feature interaction relationships on different meta-paths and learn the final embedding of nodes.Experimental results on three widely used datasets showed that the AHNN model could significantly outperform the state-of-the-art models. | Jintao Zhang Quan Xu | 2021 | Big Data Mining and Analytics2021，4，4： | 3 |
| 18 | A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks显示文摘There are many algorithms for solving complex problems in supervised manner. However, unsupervised tasks are more common in real scenarios. Inspired by the idea of granular computing and the characteristics of human cognitive process, this paper proposes a complex tasks decomposition mechanism based on Density Peaks Clustering(DPC) to address complex tasks with an unsupervised process, which simulates the multi-granular observation and analysis of human being. Firstly, the DPC algorithm is modified to nullify its essential defects such as the difficulty of locating correct clustering centers and classifying them accurately. Then, the improved DPC algorithm is used to construct the initial decomposition solving space with multi-granularity theory. We also define subtask centers set and the granulation rules to guide the multi-granularity decomposing procedure. These rules are further used to decompose the solving space from coarse granules to the optimal fine granules with a convergent and automated process. Furthermore, comprehensive experiments are presented to verify the applicability and veracity of our proposed method in community-detection tasks with several benchmark complex social networks.The results show that our method outperforms other four state-of-the-art approaches. | Ziling Pang Guoyin Wang Jie Yang | 2018 | Big Data Mining and Analytics2018，1，3： | 3 |
| 19 | Applications of Deep Learning to MRI Images:A Survey显示文摘Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tissue contrast, in recent years, Magnetic Resonance Imaging(MRI) has been attracting increasing attention. With the development of deep learning, many innovative deep learning methods have been proposed to improve MRI image processing and analysis performance. The purpose of this article is to provide a comprehensive overview of deep learning-based MRI image processing and analysis. First, a brief introduction of deep learning and imaging modalities of MRI images is given. Then, common deep learning architectures are introduced. Next, deep learning applications of MRI images, such as image detection, image registration, image segmentation, and image classification are discussed. Subsequently, the advantages and weaknesses of several common tools are discussed, and several deep learning tools in the applications of MRI images are presented.Finally, an objective assessment of deep learning in MRI applications is presented, and future developments and trends with regard to deep learning for MRI images are addressed. | Jin Liu Yi Pan Min Li Ziyue Chen Lu Tang Chengqian Lu Jianxin Wang | 2018 | Big Data Mining and Analytics2018，1，1： | 3 |
| 20 | Bursty and Hierarchical Structure in Streams显示文摘 | Jon Kleinberg | 2003 | Data Mining and Knowledge Discovery2003，，4： | 3 |
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